Impact of institutional pressures and dynamic capabilities on sustainability performance of oil and gas sector
Bibliographic record
Abstract
Purpose Globally, the oil and gas (OG) industries are under pressure from numerous stakeholders for their sustainable operations against the backdrop of climate change, ecological damage and social challenges. Drawing on the twin theoretical frameworks of the institutional theory and dynamic capability perspective, this study aims to examine the impact of the institutional pressures and dynamic capabilities on the overall sustainability performance of OG industry. Design/methodology/approach This study uses survey method to analyze the responses from 275 middle management professionals of OG industry in India using partial least squares structural equation modeling. Further, focused group discussions with the select industry leaders validate the empirical findings of this study. Findings The research reveals that both institutional pressures and firm’s dynamic capabilities have significant positive impact on its economic and environmental performances in OG sector in India. However, they do not have any impact on social performance, unlike earlier findings. Research limitations/implications The main limitation of the study is generalizability of the findings, given the cross-sectional design of the study. Practical implications Insights of this study will help regulators and policymakers in formulating effective regulatory and policy frameworks, besides creating awareness amongst the organizations to simultaneously focus on all the three aspects of sustainability performance. Originality/value The research has bearing on policy formulation and creating a regulatory ecosystem to ensure overall sustainability performance of OG industry in India.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".